Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users

Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US$0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US$9.

High risk Notes: Adapted from the American Diabetes Association 1,2 , Lindstrom and Tuomilehto 3 , Fauzi et al. 4 , and Rokhman et al. 5 Supplementary Table 2: Screening questionnaire and scoring system (Bahasa Indonesia)

Details and results of the randomized framing
To account for a potential desirability bias in our survey, i.e., individuals may only report complying with the received recommendation because they expect this to be the socially desirable answer, we randomized two different framings of the same question.
Framing 1: After completion of the test you received a recommendation to meet with a physician or GP to request a professional blood test for diabetes.Since it has already been 6 weeks since the completion of the risk test and given the urgency of a high diabetes risk, we assume that you have already scheduled an appointment with a doctor for a professional diabetes screening.
Indeed, I have already scheduled an appointment.
No, but I plan to schedule an appointment.
I don't plan to schedule an appointment.
I don't need an appointment since I already know that I have diabetes.
Framing 2: After completion of the test you received a recommendation to meet with a physician or GP to request a professional blood test for diabetes.Since you completed the online diabetes risk test only 6 weeks ago, we assume that you may not have had sufficient time to schedule an appointment with a doctor for a professional diabetes screening.
Indeed, but I plan to schedule an appointment I don't plan to schedule an appointment.
I have already scheduled an appointment I don't need an appointment since I already know that I have diabetes.
Supplementary Table 8 reports the results of a chi-squared test to assess whether the response pattern to the question about the professional blood test differs between the two framings presented above.
The results suggest that this is not the case, since the null-hypothesis of no significant correlation cannot be rejected (p-value 0.494).
Supplementary Table 8 together, it seems that we over-proportionally attracted individuals who had at least once been tested for high blood sugar levels and had a positive test outcome.Similarly, the share of individuals having ever been diagnosed with high blood pressure or taking anti-hypertensive medication in our sample is significantly higher than in the benchmark population (33% vs. 16%).The same holds true for the risk factors obesity, daily physical activity, and daily fruit consumption.On average, the individuals in our sample are more likely to be obese, less likely to carry out sufficient physical activity, and less likely to consume fruits or vegetables on a daily basis compared to the benchmark population.Only the prevalence of current smokers is significantly lower in our sample than in the overall population.
Supplementary Table 12 Notes: 1 Statistical differences in the distribution of categorical variables (age groups, BMI catgeories, smoking status) are assessed via Pearson's chi-squared test and significance stars are added to each category. 2The data for the diabetes and pre-diabetes prevalence rates come from the full Indonesian sample above the age of 35, since data per province and age category was not available. 3The BMI classifications are those provided in RISKESDAS.They differ from the scale used in the screening questionnaire, which relies on the risk-scale for BMI cut-off points for Asian populations provided by the World Health Organization Expert Consulation 10 , in which overweight is classified as having a BMI above 23 instead of 25 and obesity as having a BMI above 27.5.*** p<0.01, ** p<0.05, * p<0.1.
hence that the prevalence rate of diabetes in the "medium risk" and "low risk" group is 0%, whereas the prevalence rate in the "high risk" group is 80%.This sensitivity measure is well in line with that identified by Harbuwono et al. 12 for the American Diabetes Association risk test in Indonesia and by Rokhman et al. 5 for the Indonesian FINDRISC, who estimate a sensitivity of between 63% and 93%, depending on the cut-off level.
Second, we account for some form of self-selection, since we attracted a dis-proportionally high share of individuals that had ever been told they had high blood sugar levels (50% of participants) and a significant share that already had a diabetes diagnosis.Hence, to reflect the 34% of individuals with a high risk who indicated they did not need to conduct a professional follow-up screening since they already knew they had diabetes, we assume that 50% of the 80% of individuals that have diabetes (given the first assumption) are already aware of their diagnosis.This leads to a total of 40% of those individuals with a high risk already being diagnosed; slightly higher than the 37% identified in our study.This self-selection for campaign participation also implies that the total prevalence rate of diabetes in the campaign-participating population is slightly higher than in the non-participating population.Since the weighted prevalence rate for both groups (participating and not-participating) must be equal to the overall prevalence rate of 14% (reflecting the overall diabetes prevalence rate in Indonesia for the population aged 35 and above as identified in the RISKESDAS data), the prevalence rate of diabetes in the non-participating group is reduced to 13.78% and the ratio of diagnosed to undiagnosed cases is slightly lower (24.4%)than in the overall population.
Third, reflecting on the results of the follow-up survey, we assume that individuals with a "high risk" who are unaware of their disease status follow up with a professional diabetes screening in 40% of the cases (slightly less than the 43% found in our study).
Integrating the former assumptions, the cost and effectiveness measures and the risk distribution identified in our study, alongside the prevalence rates of diagnosed and non-diagnosed diabetes according to the RISKESDAS survey, leads to the final decision-tree depicted in Supplementary Figure 3.
We model the decision tree-flow for the total population of 25 million Javanese Facebook users above the age of 35 and as a monthly repeating intervention over the course of one year, i.e., all individuals who do not participate in the online screening questionnaire in the first (second, third etc.) month enter the decision-tree again in the second (third, fourth etc.) month.At the end of the decision-tree, an individual can have one of the following status: i) healthy, ii) diagnosed diabetes, or iii) undiagnosed diabetes.To be precise, the important difference between the results of the screening vs. the non-screening scenario is the distribution of individuals that have diabetes (14% of the total population) between the two states "diagnosed diabetes" and "undiagnosed diabetes".
The results of this repeated decision tree simulation are presented in Supplementary Table 14.
The first row presents the results associated with the parameter assumptions outlined above ((i) the sensitivity of the online screening questionnaire, (ii) the share of individuals in the high risk group who are aware of their diabetes status, and (iii) the share of individuals with a high risk and unaware of their diagnosis who follow up with professional screening).We conduct multiple sensitivity checks by modifying the three above assumptions, first one by one and then all together, to see how they impact the final cost-effectiveness.The modified parameter is marked in bold in each row.
The main analysis reveals that the hypothetical up-scaling of the campaign to the whole of Java over the period of one year could lead to about 1.7 million users participating in the online screening, of whom about 250,000 would continue with the professional follow-up screening, and finally to the diagnosis of almost 170,000 previously undetected diabetes cases.This corresponds to an increase from 25% to 29% of diagnosed cases relative to all cases, i.e., an increase of 16%.While the share might still seem small, the absolute number is large, especially in light of the low cost and low effort needed to implement an online health campaign.This low cost is further confirmed when we look at the total cost of the proposed intervention (including the professional follow-up screening), which is slightly higher than US$1.5 million.Dividing the total cost by the 170,000 newly diagnosed cases, the cost of detecting one more previously undiagnosed person amounts to approximately US$9.
Modifying several of the input parameters shows that the cost per diagnosed person barely surpasses a threshold of US$15.Even in the most pessimistic scenario (last row), with an assumed test-sensitivity of only 60%, with 75% of individuals receiving a high-risk score and having diabetes already knowing they have the disease, and only 20% of individuals following up with the recommendation, the cost for one newly diagnosed person amounts to only US$37.Notes: Supplementary Table 14 shows the results from the repeated decision tree simulations.The first row presents the cost-effectiveness results when the input parameters are set as discussed in Section F. The following rows present the results when the input parameters are modified.The modified parameters are indicated in bold.In the last two rows, all three input parameters are modified to present a best-case and worst-case scenario.

Table 3 :
Age, gender and location distribution by ad Supplementary Table4presents the summary statistics for all questionniares that were started, with duplicates.Supplementary Table6presents the results of the comparisons between the full sample of participants that completed the screening, the e-mail providers and the sample of participants that completed the follow-up survey.*** p<0.01, ** p<0.05, * p<0.1. 1Statistical differences in the distribution of categorical variables (age groups, smoking status, risk groups) are assessed via Pearson's chi-squared test and significance stars are added to each category.
5,2, Lindstrom and Tuomilehto 3 , Fauzi et al.4, and Rokhman et al.5Supplementary Material 2: Additional summary statistics Notes: Supplementary Table3presents the age, gender and location distribution for the total sample and by ad. Allvariables are binary variables, hence, the summary statistics are shares of the respective variable.

Table 7 :
Email and follow-up survey response rates across ad designs and as a function of risk score Notes: Robust standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1.

:
Framing experiment Robust standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1.The number of completed survey questionnaires counted as "conversion" are derived from the reduced sample without duplicated questionnaires (N=1,469) of which 1,443 could be linked to the referring ad theme.The missing 26 could not be linked to the referring ad theme due to tracking restrictions. Notes:

Table 10 :
Results -Ad effectiveness (Marginal effects -Logit model) ** p<0.01, ** p<0.05, * p<0.1.The number of completed survey questionnaires counted as "conversion" are derived from the reduced sample without duplicated questionnaires (N=1,469) of which 1,443 could be linked to the referring ad theme.The missing 26 could not be linked to the referring ad theme due to tracking restrictions.*Mean of dependent var.refers to the mean of the dependent variable in the reference group.
Notes:The Table shows the marginal effects of the logit models showsn in equation (1) and (2) in the main manuscript.Columns (2) and (6) correspond to the relative effects presented in Figures2 and 3in the main manuscript.Robust standard errors in parentheses.*

:
Comparison of the sample with benchmark populations